Methods for the detection and identification of multiple components in a sample using the principles of liquid chromatography (LC) and spectrophotometry are well known to those skilled in the art. The identification of specific unknown components in the sample solution first requires a physical separation of any mixed components using liquid chromatography. The sample is eluted or washed through an LC column using a mobile phase of solvents known to be appropriate for the particular separation sought and system used. Separation is achieved with the LC column based upon a particular component's adsorptive affinity to the media packed in the LC column. The eluted sample is then analyzed using UV-VIS spectrophotometry. The particular chemical structure of the analyte when subjected to radiation in the ultraviolet-visible (UV-VIS) range of 200-800 nm will have a characteristic spectral response due to the absorption of radiation when detected with a photodiode-array (PDA) detector. The (PDA) detector measures the transmitted energy and converts it into absorption units (AU) as a function of elution time (tR) and wavelength (λ).
Detection of impurities poses a problem when the particular chemical structure of the impurities is such that they become co-eluted with the analyte of interest. When this occurs the impurity is often difficult to detect because the chromatomographic separation does not sufficiently resolve the different components and their spectral peaks are overlapped. In the case of a single wavelength UV/visible detector one might see a “shoulder”, “valley” or “excessive tailing” in the chromatographic peak to suspect the presence of an impurity. Moreover the absence of these features on the chromatographic peak does not assure that the component represented by the peak is pure. The impurity could simply be cloaked or “not seen” within the larger peak that is detected by the spectrophotometer because the chromatographic resolution was too low or the purity concentration was too low.
Several approaches have been taken to detect the presence of an impurity co-eluting with the analyte peak. M. V. Gorenstein, et al., “Detecting Co-Eluted Impurities by Spectral Comparison”, LC-GC. 12, No. 10, 768-772 (1994), incorporated herein by reference, discloses a two-step spectral-contrast technique which first generates vector angles which measure the spectral heterogeneity of a given peak and the shape difference between two spectra. Then a quantification for non-ideal effects such as noise contribution is made and assigned as a threshold vector angle. These two angles are compared along an analyte's chromatogram. The procedure correlates the difference between the two angles with detection of an impurity in a chromatographic peak. However, in order to accomplish this there must be a baseline correction for each spectrum within the peak which involves interpolation of peak “start” and “end” spectra to obtain a series of underlying baseline spectra. In addition, the vector angles used to determine whether an impurity exists do not directly calculate or quantify the spectral error in the measured absorbence units caused by the impurity. Also, such a technique requires separate calibration runs to establish a threshold, and will have difficulties with multiple impurities.
Y. Hu, et al., “Assessment of Chromatographic Peak Purity By Means Of Artificial Neural Networks” J. Chromatogr. 734, 259-270 (1996), incorporated herein by reference, discloses a three-layer neural network for detecting the purity of a chromatographic peak. The method relies on applying a neural network to the difference between the spectra within the chromatographic peak to be analyzed. Applications of neural networks are known to those skilled in the art, and as disclosed by Hu in this application, involve training sets of data for the front half of each peak and testing these data sets for recognition responses against the back half of each peak using artificial neural network techniques. This approach strongly depends on the retention time and number of impurities with respect to the main components(s) due to the somewhat arbitrary partitioning between a front and back part of a chromatogram. Another disadvantage to this neural network approach is that approximately half of the data associated with a component's chromatogram and spectra are used which can compromise the detection sensitivity for analytes where the chromatographic retentions and spectra of the main component substance and impurity are strongly overlapped. Moreover, this approach is incapable of performing a quantitative analysis concerning impurity level.
What is desired, therefore, is a method to detect and quantify an impurity from an LC peak using UV-VIS spectrophotometry with a PDA detector where a measure of peak impurity can be reported in natural units across a whole chromatographic peak and where noise filtering is automatically built-in.